Computing for remote sensing
### Working language
Portuguese and English
### Goals
It is intended that students acquire skills and knowledge of programming/scientific computing that will allow them to develop tools and applications dedicated to the areas of Remote Sensing (RD).
### Learning outcomes and skills
The program covers the Python language as a programming language, as well as the use of scientific computing libraries for manipulation and visualization of geospatial information for the development of applications.
### Working mode
In person
### Prerequisites (prior knowledge) and co-requisites (concurrent knowledge)
No prerequisites.
### Program
Introduction to the Python language
Introduction to the command line for interactive computing – IPython
Data Types (variables of type int, float, byte…, strings, lists, dictionaries…)
Control flow (loops, if-then conditions)
Code organization (functions, modules, packages)
File writing and reading, data input-output
Introduction to the _numpy_ module
Understand data structuring with N-dimensions
array creation
Array indexing, joining and cutting with indexes, masks
Basic operations and manipulation of N-dimensional arrays
Introduction to the 2D visualization module – _matplotlib_
Control of colors, axes and legends
Creating scatter, line, and bar charts
Statistical graphs, histograms
Level curves, 2.5D visualization
Sub-figures, graphic organization
Introduction to the _s__ci__p__y_ module for scientific computing
trigonometric functions
statistical functions
Linear Algebra, vectors and matrices
Linear, polynomial and spline interpolation
data input-output
Visualization of georeferenced information – _b__asemap_ and _c__artopy_
map creation
cartographic projections
Coastlines, political boundaries, land-sea, lakes and rivers
Mapping of vector information through shapefiles
Data structuring in time series and dataframes – module _p__andas_
Data input-output in pandas
Structured information 1D (series) and 2D (dataframes)
Data organization, aggregation and indexing criteria
Computing and analyzing information in pandas
Control of dates and times, module _astropy_
2D visualization and matplotlib integrated in Pandas
Multi-dimensional arrays and datasets with _pandas_ and _xarray_
Input-output of structured information in netCDF
Data indexing and selection
Extraction and manipulation of variables
Statistical analysis by dimensions and time series
Reorganization and visualization
### Mandatory Bibliography
Mark Lutz; [Programming Python](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000228063 "Programming Python (Opens in a new window)"). ISBN: 0-596-00085-5
### Complementary Bibliography
Mark Lutz; [Python pocket reference](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000189758 "Python pocket reference (Opens in a new window)"). ISBN: 978-1-56592-500-7
Matt A. Wood; [Python and Matplotlib essentials for scientists and engineers](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000855875 "Python and Matplotlib essentials for scientists and engineers (Opens in a new window )"). ISBN: 978-1-62705-619-9
Hans Peter Langtangen; [A primer on scientific programming with Python](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000291612 "A primer on scientific programming with Python (Opens in a new window)" ). ISBN: 978-3-642-02474-0
### Teaching methods and learning activities
Classes are based on Powerpoint presentations and Notebooks with practical exercises exemplifying the use of the various modules addressed.
### Software
Python interpreter
virtualbox
### Type of evaluation
Evaluation by final exam
### Assessment Components
Exam: 100.00%
**Total:**: 100.00
### Occupation Components
Self-study: 50.00 hours
Frequency of classes: 50.00 hours
**Total:**: 100.00
### Get Frequency
Class attendance is mandatory.
Students may lose attendance if they exceed the number of absences provided by law.
### Final classification calculation formula
Final exam (100%).
More information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479402
Presential
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Computing for remote sensing
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HaDEA. Neither the European Union nor the granting authority can be held responsible for them. The statements made herein do not necessarily have the consent or agreement of the ASTRAIOS Consortium. These represent the opinion and findings of the author(s).
